A high-performance variable sampling clock generator for the nearest neighbor sampling technique

Penelitian ini bertujuan untuk mengetahui gambaran dan hubungan self efficacy, coping stress dan prestasi akademik mahasiswa semester awal Program Studi Psikologi Fakultas Kedokteran Universitas Lambung Mangkurat. Subjek penelitian berjumlah 60 orang. Tekhnik pengambilan data dengan cara purposive sampling. Alat pengumpul data yang digunakan adalah skala self efficacy dan skala coping stress. Untuk prestasi akademik data dikumpulkan dengan melihat indeks prestasi akademik (IPK) semester pertama. Data yang terkumpul dianalisis dengan analisis regresi berganda. Hasil penelitian menunjukkan : (1) tidak ada hubungan yang sangat signifikan antara variabel self efficacy, coping stress dan prestasi akademik , (2) sumbangan prediktor (R2) self efficacy dan coping stress sebesar 2%, (3) rata-rata mahasiswa Program Studi Psikologi angkatan 2012 memiliki self efficacy yang tergolong tinggi, coping stress yang tergolong sedang dan prestasi akademik yang tergolong sedang.Kata kunci : self efficacy, coping stress, dan prestasi akademik Aim to determine relationship between self efficacy, coping stress and achievement academic in first semester college student of Psychology Study Program of Medical Faculty of Lambung Mangkurat University. Method respondents as many as 60 first semester college students. Sampling technique by using purposive sampling. Data collection by using self efficacy scale, coping of stress scale and achievement academic indeks of first semester. Data analyzed by multiple regression. Results the relationship between self efficacy, coping of stress and achievement academic is not significant.. Self efficacy and coping of stress contribute 2% to achievement academic. Conclusion Odd semester college student in 2012 has high performance in self efficacy, middle in coping of stress and middle in achievement academic. Keywords: self efficacy, coping of stress, achievement academic

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Comparison of Work Motivation and Working Conditions to The Performance of Msmes Employees Before and During Covid-19

JIMF (JURNAL ILMIAH MANAJEMEN FORKAMMA) ◽

10.32493/frkm.v4i3.11526 ◽

2021 ◽

Vol 4 (3) ◽

Author(s):

Agus Dwi Cahya ◽

Bagus Disfantoro ◽

Khusniah Khusniah

Keyword(s):

Working Conditions ◽

Comparative Research ◽

Work Motivation ◽

Employee Performance ◽

Sampling Technique ◽

Total Sample ◽

Performance Variable ◽

Significant Difference ◽

Saturated Sample ◽

Research Type

“The purpose of this study was to compare the effect of work motivation and working conditions on the performance of MSME employees before and during the Covid-19 pandemic. The researcher uses a comparative research type with a quantitative approach. Techniques Data collection is done using a questionnaire with the linkert method. And the sampling technique used is to use a saturated sample (total sampling). The total sample obtained is 37 employees from several MSMEs. In the test of the effect of work motivation, work motivation has no significant effect on employee performance. Variable working conditions have a significant effect on employee performance. In the F test there is an influence between work motivation and working conditions on employee performance. And in the comparison test, there is a significant difference between work motivation and working conditions before and during covid-19. While in the employee performance variable there is no significant difference between employee performance before and during Covid-19”.

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Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

Diagnostics ◽

10.3390/diagnostics11101870 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1870

Author(s):

Yaghoub Pourasad ◽

Esmaeil Zarouri ◽

Mohammad Salemizadeh Parizi ◽

Amin Salih Mohammed

Keyword(s):

Breast Cancer ◽

High Performance ◽

Nearest Neighbor ◽

Support Vector ◽

Ultrasound Images ◽

Morphological Operations ◽

K Nearest Neighbor ◽

Diagnose Breast Cancer ◽

Premature Deaths ◽

Infected Area

Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor’s location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.

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The Effect of Compensation on Employee Performance at Kreasindo Jaya Abadi

FOCUS ◽

10.37010/fcs.v1i2.356 ◽

2021 ◽

Vol 1 (2) ◽

pp. 80-83

Author(s):

Elis Silmi ◽

Rudy Susanto ◽

Ismail Dwi Cahyo

Keyword(s):

Linear Regression ◽

Random Sampling ◽

Research Method ◽

Quantitative Research ◽

Employee Performance ◽

Sampling Technique ◽

Linear Regression Equation ◽

Simple Linear Regression ◽

Performance Variable ◽

Moderate Relationship

The purpose of this study was to determine the effect of compensation on employee performance. The method used is a quantitative research method with the number of samples used is 54 respondents. The sampling technique was done by random sampling. The results of the study: The correlation coefficient (rxy) was 0.46. This shows that there is a moderate relationship between the variables of Compensation and Employee Performance at Kreasindo Jaya Abadi; Compensation variable contributes to Employee Performance variable by 21.16% and the remaining 78.84% is contributed by other variables; We get a simple linear regression equation Y = 18.30 + 0.5082X. The results of the hypothesis analysis show the value of t count > t table (3.7353 > 1.6747), so H0 is rejected and Ha is accepted. So, it can be concluded that there is a significant influence between the Compensation variable and the Employee Performance variable.

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Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data

Journal of Integrative Bioinformatics ◽

10.1515/jib-2016-304 ◽

2016 ◽

Vol 13 (5) ◽

Cited By ~ 4

Author(s):

Malik Yousef ◽

Waleed Khalifa ◽

Loai AbdAllah

Keyword(s):

High Performance ◽

Nearest Neighbor ◽

Ensemble Clustering ◽

Distance Metric ◽

K Nearest Neighbor ◽

Clustering Ensemble ◽

Data Mining Algorithms ◽

Knn Classifier ◽

Knn Classification ◽

Mining Algorithms

SummaryThe performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN). In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that EC-kNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

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High‐performance implementation of a two‐bit geohash coding technique for nearest neighbor search

Concurrency and Computation Practice and Experience ◽

10.1002/cpe.6029 ◽

2020 ◽

Author(s):

Varalakshmi M ◽

Amit P. Kesarkar ◽

Daphne Lopez

Keyword(s):

High Performance ◽

Nearest Neighbor ◽

Nearest Neighbor Search ◽

Neighbor Search

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SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning

Electronics ◽

10.3390/electronics8111323 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1323 ◽

Cited By ~ 1

Author(s):

Donald L. Hall ◽

Ram M. Narayanan ◽

David M. Jenkins

Keyword(s):

Deep Learning ◽

Software Defined Radio ◽

High Performance ◽

Nearest Neighbor ◽

Indoor Localization ◽

Signal Propagation ◽

Multipath Channel ◽

K Nearest Neighbor ◽

Positioning Systems ◽

Channel Characteristics

Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.

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Analysis of Factors Influencing Financial Performance of Savings and Credit Co-operative Societies in Lesotho: Evidence From Maseru District

International Journal of Financial Research ◽

10.5430/ijfr.v10n2p121 ◽

2019 ◽

Vol 10 (2) ◽

pp. 121

Author(s):

Goodluck A. Mmari ◽

Lebitso C. Thinyane

Keyword(s):

Financial Performance ◽

High Performance ◽

Sampling Technique ◽

Simple Random Sampling ◽

Descriptive Statistics ◽

Poor People ◽

Cross Sectional ◽

Fixed Assets ◽

Mathematical Equations

SACCOS play a major role of providing financial access to poor people who are excluded from the services of Formal Financial Institutions (FFIs). However, they also face number of challenges which may affect their performance. Most of the previous studies in the area of SACCOS did not concentrate on their performance. The aim of this study therefore was to assess performance of SACCOS in Maseru District, Lesotho. The study adopted a cross-sectional research design where data were collected at one point in time. A sample size of 369 respondents was computed by the use of formula by Yamane (1967). Respondents in the sample were selected by using simple random sampling technique. However, respondents from individual SACCOS were proportional to the total number of members in particular SACCOS. This was done in order to make the sample representative of all SACCOS in the study area. Analyses of data were done by using different techniques which include: mathematical equations (i to vii); different financial ratios; tables; graphs; bar charts and other types of descriptive statistics like mode and percentages. It was found that socio economic characteristics of members were supportive to financial performance of the SACCOS. Furthermore, SACCOS in the study area achieved high performance in terms of ratios of members’ capital; loan delinquency; volumes of savings in the SACCOS; and growth of total assets. On the other hand, the SACCOS realised poor financial performance in terms of ratio of fixed assets to total assets; and share capital owned by members.

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Improving undersampling-based ensemble with rotation forest for imbalanced problem

TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES ◽

10.3906/elk-1805-159 ◽

2019 ◽

pp. 1371-1386 ◽

Cited By ~ 1

Author(s):

Huaping GUO ◽

Xiaoyu DIAO ◽

Hongbing LIU

Keyword(s):

High Performance ◽

State Of The Art ◽

Class Imbalance ◽

Imbalanced Data ◽

Ensemble Methods ◽

Sampling Technique ◽

Robust Methods ◽

Limited Data ◽

Minority Class ◽

Rotation Forest

As one of the most challenging and attractive issues in pattern recognition and machine learning, the imbalanced problem has attracted increasing attention. For two-class data, imbalanced data are characterized by the size of one class (majority class) being much larger than that of the other class (minority class), which makes the constructed models focus more on the majority class and ignore or even misclassify the examples of the minority class. The undersampling-based ensemble, which learns individual classifiers from undersampled balanced data, is an effective method to cope with the class-imbalance data. The problem in this method is that the size of the dataset to train each classifier is notably small; thus, how to generate individual classifiers with high performance from the limited data is a key to the success of the method. In this paper, rotation forest (an ensemble method) is used to improve the performance of the undersampling-based ensemble on the imbalanced problem because rotation forest has higher performance than other ensemble methods such as bagging, boosting, and random forest, particularly for small-sized data. In addition, rotation forest is more sensitive to the sampling technique than some robust methods including SVM and neural networks; thus, it is easier to create individual classifiers with diversity using rotation forest. Two versions of the improved undersampling-based ensemble methods are implemented: 1) undersampling subsets from the majority class and learning each classifier using the rotation forest on the data obtained by combing each subset with the minority class and 2) similarly to the first method, with the exception of removing the majority class examples that are correctly classified with high confidence after learning each classifier for further consideration. The experimental results show that the proposed methods show significantly better performance on measures of recall, g-mean, f-measure, and AUC than other state-of-the-art methods on 30 datasets with various data distributions and different imbalance ratios.

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